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Generating and navigating proteome maps using mass spectrometry

Key Points

  • Complete proteome reference maps, which contain validated, mass spectrometry (MS)-derived, reference fragment ion spectra for unique peptides for each protein of a proteome, are becoming an indispensible resource to quantify the dynamic behaviour of a proteome, or subsets thereof, under multiple conditions.

  • Despite enormous technical advances in shotgun proteomics, which is the main MS-based proteome technology today, the generation of such reference maps has been challenging and expensive.

  • The use of synthetic peptide libraries representing unique peptides for each protein of a proteome, and the fragment ion spectra derived from these compounds, are a good basis for the generation of complete proteome reference maps.

  • To generate and use proteome reference maps, the precise level of resolution of proteome analysis needs to be considered and carefully defined. Specifically, resolving splice forms or differentially modified proteins poses different challenges compared to the quantification of the primary translation products of a gene locus.

  • New MS techniques that use proteome reference maps as prior information support the quantification of complete or partial proteomes at unprecedented levels of reproducibility, sensitivity and dynamic range. Reminiscent of microarray-based gene expression analysis, such capabilities will allow the quantitative monitoring of dynamic protein expression in different cells and tissues at different states, and are particularly important for systems biology and clinical research.

  • Proteomics is moving from an era focused on the perpetual discovery of proteins towards an era of determining the relevant biological information about proteins. Complete proteome maps will be an essential element to realize this fundamental transition.

Abstract

Proteomes, the ensembles of all proteins expressed by cells or tissues, are typically analysed by mass spectrometry. Recent technical and computational advances have greatly increased the fraction of a proteome that can be identified and quantified in a single study. Current mass spectrometry-based proteomic strategies have the potential to reproducibly, accurately, quantitatively and comprehensively measure any protein or whole proteomes from cells and tissues at different states. Achieving these goals will require complete proteome maps and analytical strategies that use these maps as prior information and will greatly enhance the impact of proteomics on biological and clinical research.

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Figure 1: Development of the proteomics field.
Figure 2: Navigating proteome maps to generate contextual information on the proteome.
Figure 3: Description of dynamic processes using static proteome maps.

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References

  1. Fleischmann, R. D. et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512 (1995).

    Article  CAS  PubMed  Google Scholar 

  2. Liolios, K. et al. The genomes on line database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res. 38, D346–D354 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Curwen, V. et al. The Ensembl automatic gene annotation system. Genome Res. 14, 942–950 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  PubMed  Google Scholar 

  6. Lockhart, D. J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotech. 14, 1675–1680 (1996).

    Article  CAS  Google Scholar 

  7. Chu, S. et al. The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998).

    Article  CAS  PubMed  Google Scholar 

  8. Spellman, P. T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Perou, C. M. et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl Acad. Sci. USA 96, 9212–9217 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  CAS  PubMed  Google Scholar 

  11. Liu, H., Sadygov, R. G. & Yates, J. R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004). Description of the saturation effect associated with shotgun proteomics experiments.

    Article  CAS  PubMed  Google Scholar 

  12. Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotech. 25, 576–583 (2007). Iterative proteomics approach generating the first large-scale eukaryotic proteome catalogue, including experimentally validated proteotypic peptides.

    Article  CAS  Google Scholar 

  13. Carninci, P. et al. Targeting a complex transcriptome: the construction of the mouse full-length cDNA encyclopedia. Genome Res. 13, 1273–1289 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Cochrane, G. R. & Galperin, M. Y. The 2010 Nucleic Acids Research database issue and online database collection: a community of data resources. Nucleic Acids Res. 38, D1–D4 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Washburn, M. P., Wolters, D. & Yates, J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotech. 19, 242–247 (2001). The first paper to demonstrate the power of shotgun proteomics in achieving a high proteome coverage.

    Article  CAS  Google Scholar 

  17. Grobei, M. A. et al. Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function. Genome Res. 19, 1786–1800 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Qeli, E. & Ahrens, C. H. PeptideClassifier for protein inference and targeted quantitative proteomics. Nature Biotech. 28, 647–650 (2010). Description of the peptide information content and its application for protein inference, data integration and peptide selection for targeted quantitative proteomics.

    Article  CAS  Google Scholar 

  19. Wasinger, V. C. et al. Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 16, 1090–1094 (1995).

    Article  CAS  PubMed  Google Scholar 

  20. Wilkins, M. R. et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol. Genet. Eng. Rev. 13, 19–50 (1996).

    Article  CAS  PubMed  Google Scholar 

  21. Jensen, O. N. Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol. 8, 33–41 (2004).

    Article  PubMed  CAS  Google Scholar 

  22. Gavin, A. C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Beadle, G. W. & Tatum, E. L. Genetic control of biochemical reactions in Neurospora. Proc. Natl Acad. Sci. USA 27, 499–506 (1941).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gerstein, M. B. et al. What is a gene, post-ENCODE? History and updated definition. Genome Res. 17, 669–681 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Nesvizhskii, A. I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nature Methods 4, 787–797 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Resing, K. A. et al. Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics. Anal. Chem. 76, 3556–3568 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Zhang, B., Chambers, M. C. & Tabb, D. L. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J. Proteome Res. 6, 3549–3557 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Searle, B. C., Turner, M. & Nesvizhskii, A. I. Improving sensitivity by probabilistically combining results from multiple MS/MS search methodologies. J. Proteome Res. 7, 245–253 (2008).

    Article  CAS  PubMed  Google Scholar 

  31. Ma, Z. Q. et al. IDPicker 2.0: improved protein assembly with high discrimination peptide identification filtering. J. Proteome Res. 8, 3872–3881 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li, Y. F. et al. A bayesian approach to protein inference problem in shotgun proteomics. J. Comput. Biol. 16, 1183–1193 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gerster, S., Qeli, E., Ahrens, C. H. & Bühlmann, P. Protein and gene model inference based on statistical modeling in k-partite graphs. Proc. Natl Acad. Sci. USA 107, 12101–12106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tabb, D. L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nature Biotech. 27, 633–641 (2009). A multi-laboratory study demonstrating the reproducibility of multiplexed, MRM-based assays across laboratories and instrument platforms.

    Article  CAS  Google Scholar 

  36. Dieguez-Acuna, F. J. et al. Characterization of mouse spleen cells by subtractive proteomics. Mol. Cell. Proteomics 4, 1459–1470 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Chu, D. S. et al. Sperm chromatin proteomics identifies evolutionarily conserved fertility factors. Nature 443, 101–105 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. de Godoy, L. M. et al. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system. Genome Biol. 7, R50 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186 (2006).

    Article  CAS  PubMed  Google Scholar 

  40. de Godoy, L. M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008). A high-quality data set of the most extensive proteome coverage of a model organism using the latest MS technology.

    Article  CAS  PubMed  Google Scholar 

  41. Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–765 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Bell, A. W. et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nature Methods 6, 423–430 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Gouw, J. W., Krijgsveld, J. & Heck, A. J. Quantitative proteomics by metabolic labeling of model organisms. Mol. Cell. Proteomics 9, 11–24 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Gygi, S. P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotech. 17, 994–999 (1999). An approach for the accurate quantification and identification of individual peptides or proteins in complex mixtures.

    Article  CAS  Google Scholar 

  46. Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002). Description of a quantification method that is based on the incorporation of stable-isotope-labelled amino acids into proteins in cell culture.

    Article  CAS  PubMed  Google Scholar 

  47. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Ross, P. L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 3, 1154–1169 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156 (2006). A label-free protein quantification method that relies on the three precursor ions with the highest ion intensity.

    Article  CAS  PubMed  Google Scholar 

  50. Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nature Biotech. 25, 117–124 (2007). Description of a method that uses corrected spectral counts for estimating protein abundance.

    Article  CAS  Google Scholar 

  51. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b-range mass accuracies and proteome-wide protein quantification. Nature Biotech. 26, 1367–1372 (2008).

    Article  CAS  Google Scholar 

  52. Griffin, N. M. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nature Biotech. 28, 83–89 (2010).

    Article  CAS  Google Scholar 

  53. Dong, M. Q. et al. Quantitative mass spectrometry identifies insulin signaling targets in C. elegans. Science 317, 660–663 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Baek, D. et al. The impact of microRNAs on protein output. Nature 455, 64–71 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schrimpf, S. P. et al. Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol. 7, e48 (2009).

    Article  PubMed  CAS  Google Scholar 

  56. Waanders, L. F. et al. Quantitative proteomic analysis of single pancreatic islets. Proc. Natl Acad. Sci. USA 106, 18902–18907 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gouw, J. W. et al. In vivo stable isotope labeling of fruit flies reveals post-transcriptional regulation in the maternal-to-zygotic transition. Mol. Cell. Proteomics 8, 1566–1578 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, W., Guo, T., Song, T., Lee, C. S. & Balgley, B. M. Comprehensive yeast proteome analysis using a capillary isoelectric focusing-based multidimensional separation platform coupled with ESI-MS/MS. Proteomics 7, 1178–1187 (2007).

    Article  CAS  PubMed  Google Scholar 

  59. Baerenfaller, K. et al. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320, 938–941 (2008).

    Article  CAS  PubMed  Google Scholar 

  60. Desiere, F. et al. Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry. Genome Biol. 6, R9 (2005).

    Article  PubMed  Google Scholar 

  61. Craig, R., Cortens, J. P. & Beavis, R. C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004).

    Article  CAS  PubMed  Google Scholar 

  62. Martens, L. et al. PRIDE: the proteomics identifications database. Proteomics 5, 3537–3545 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Falkner, J. A., Hill, J. A. & Andrews, P. C. Proteomics FASTA archive and reference resource. Proteomics 8, 1756–1757 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Slotta, D. J., Barrett, T. & Edgar, R. NCBI Peptidome: a new public repository for mass spectrometry peptide identifications. Nature Biotech. 27, 600–601 (2009).

    Article  CAS  Google Scholar 

  65. Nesvizhskii, A. I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 5, 652–670 (2006).

    Article  CAS  PubMed  Google Scholar 

  66. Choi, H. & Nesvizhskii, A. I. False discovery rates and related statistical concepts in mass spectrometry-based proteomics. J. Proteome Res. 7, 47–50 (2008).

    Article  CAS  PubMed  Google Scholar 

  67. Kall, L., Storey, J. D. & Noble, W. S. QVALITY: nonparametric estimation of q values and posterior error probabilities. Bioinformatics 25, 964–966 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Reiter, L. et al. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol. Cell. Proteomics 8, 2405–2417 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Gupta, N. & Pevzner, P. A. False discovery rates of protein identifications: a strike against the two-peptide rule. J. Proteome Res. 8, 4173–4181 (2009). A discussion of the two-peptide rule and its effects on protein FDRs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature Methods 4, 207–214 (2007). Explanation of the target–decoy search strategy for estimating the FDR in shotgun proteomics.

    Article  CAS  PubMed  Google Scholar 

  71. Claassen, M., Aebersold, R. & Buhmann, J. M. in 14th Annual International Conference on Research in Computational Molecular Biology 96–109 (Springer, Lisbon, Portugal, 2010).

    Google Scholar 

  72. Jaffe, J. D., Berg, H. C. & Church, G. M. Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics 4, 59–77 (2004). The first approach that uses proteomics data to improve genome annotation.

    Article  CAS  PubMed  Google Scholar 

  73. Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).

    Article  CAS  PubMed  Google Scholar 

  74. Tirumalai, R. S. et al. Characterization of the low molecular weight human serum proteome. Mol. Cell. Proteomics 2, 1096–1103 (2003).

    Article  CAS  PubMed  Google Scholar 

  75. Ausloos, P. et al. The critical evaluation of a comprehensive mass spectral library. J. Am. Soc. Mass Spectrom. 10, 287–299 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Begley, P. et al. Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum. Anal. Chem. 81, 7038–7046 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. Hunt, D. F. et al. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255, 1261–1263 (1992).

    Article  CAS  PubMed  Google Scholar 

  78. Hunt, D. F. et al. Peptides presented to the immune system by the murine class II major histocompatibility complex molecule I-Ad. Science 256, 1817–1820 (1992).

    Article  CAS  PubMed  Google Scholar 

  79. Frank, R. & Overwin, H. SPOT synthesis. Epitope analysis with arrays of synthetic peptides prepared on cellulose membranes. Methods Mol. Biol. 66, 149–169 (1996).

    CAS  PubMed  Google Scholar 

  80. Wenschuh, H. et al. Coherent membrane supports for parallel microsynthesis and screening of bioactive peptides. Biopolymers 55, 188–206 (2000).

    Article  CAS  PubMed  Google Scholar 

  81. Lam, H. et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7, 655–667 (2007).

    Article  CAS  PubMed  Google Scholar 

  82. Picotti, P. et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nature Methods 7, 43–46 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. Picotti, P., Aebersold, R. & Domon, B. The implications of proteolytic background for shotgun proteomics. Mol. Cell. Proteomics 6, 1589–1598 (2007).

    Article  CAS  PubMed  Google Scholar 

  84. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nature Biotech. 25, 125–131 (2007).

    Article  CAS  Google Scholar 

  85. Fusaro, V. A., Mani, D. R., Mesirov, J. P. & Carr, S. A. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nature Biotech. 27, 190–198 (2009).

    Article  CAS  Google Scholar 

  86. Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nature Rev. Mol. Cell Biol. 6, 577–583 (2005). Defines proteotypic peptides and their advantages for the targeted scoring of proteomes.

    Article  CAS  Google Scholar 

  87. Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655–D658 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Loevenich, S. N. et al. The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation. BMC Bioinformatics 10, 59 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Picotti, P. et al. A database of mass spectrometric assays for the yeast proteome. Nature Methods 5, 913–914 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Domon, B. & Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotech. 28, 710–721 (2010).

    Article  CAS  Google Scholar 

  92. Anderson, L. & Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006). The use of SRM for the quantification of proteins in complex mixtures.

    Article  CAS  PubMed  Google Scholar 

  93. Schmidt, A., Claassen, M. & Aebersold, R. Directed mass spectrometry: towards hypothesis-driven proteomics. Curr. Opin. Chem. Biol. 13, 510–517 (2009).

    Article  CAS  PubMed  Google Scholar 

  94. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Abbatiello, S. E., Mani, D. R., Keshishian, H. & Carr, S. A. Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin. Chem. 56, 291–305 (2010).

    Article  CAS  PubMed  Google Scholar 

  96. Picotti, P., Bodenmiller, B., Mueller, L. N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795–806 (2009). Description of a complete set of SRM assays for the quantification of any set of proteins with high throughput and quantitative accuracy in yeast.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Stahl-Zeng, J. et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell. Proteomics 6, 1809–1817 (2007).

    Article  CAS  PubMed  Google Scholar 

  98. Lange, V., Picotti, P., Domon, B. & Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008). A detailed tutorial discussing the requirements for, and applications of, SRM in targeted, quantitative proteomics.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Martin, D. B. et al. MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments. Mol. Cell. Proteomics 7, 2270–2278 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Gerber, S. A., Rush, J., Stemman, O., Kirschner, M. W. & Gygi, S. P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl Acad. Sci. USA 100, 6940–6945 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Krijgsveld, J. et al. Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics. Nature Biotech. 21, 927–931 (2003). A method for protein quantification based on stable isotope labelling of multicellular organisms.

    Article  CAS  Google Scholar 

  102. McHugh, L. & Arthur, J. W. Computational methods for protein identification from mass spectrometry data. PLoS Comput. Biol. 4, e12 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Craig, R., Cortens, J. C., Fenyo, D. & Beavis, R. C. Using annotated peptide mass spectrum libraries for protein identification. J. Proteome Res. 5, 1843–1849 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Lam, H. & Aebersold, R. Spectral library searching for peptide identification via tandem MS. Methods Mol. Biol. 604, 95–103 (2010).

    Article  CAS  PubMed  Google Scholar 

  105. Frank, A. M., Savitski, M. M., Nielsen, M. L., Zubarev, R. A. & Pevzner, P. A. De novo peptide sequencing and identification with precision mass spectrometry. J. Proteome Res. 6, 114–123 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Kim, S., Gupta, N., Bandeira, N. & Pevzner, P. A. Spectral dictionaries: integrating de novo peptide sequencing with database search of tandem mass spectra. Mol. Cell. Proteomics 8, 53–69 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002). A statistical approach for discriminating false peptide assignments from correct ones.

    Article  CAS  PubMed  Google Scholar 

  108. Kall, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4, 923–925 (2007).

    Article  PubMed  CAS  Google Scholar 

  109. Kall, L., Storey, J. D., MacCoss, M. J. & Noble, W. S. Posterior error probabilities and false discovery rates: two sides of the same coin. J. Proteome Res. 7, 40–44 (2008).

    Article  PubMed  CAS  Google Scholar 

  110. Nesvizhskii, A. I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419–1440 (2005). Discussion of the role of protein inference in shotgun proteomics and its use for deducing proteins from observed peptides.

    Article  CAS  PubMed  Google Scholar 

  111. Li, Q., MacCoss, M. J., Stephens, M. A nested mixture model for protein identification using mass spectrometry. Ann. Stat. 4, 962–987 (2010).

    Google Scholar 

  112. Guigo, R. et al. EGASP: the human ENCODE genome annotation assessment project. Genome Biol. 7 Suppl 1, S21–31 (2006).

    Article  Google Scholar 

  113. Tanner, S. et al. Improving gene annotation using peptide mass spectrometry. Genome Res. 17, 231–239 (2007). This approach applies proteogenomics to large-scale proteomics data and describes some of the related computational aspects.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Ansong, C., Purvine, S. O., Adkins, J. N., Lipton, M. S. & Smith, R. D. Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Brief Funct. Genomic. Proteomic. 7, 50–62 (2008).

    Article  CAS  PubMed  Google Scholar 

  115. Shevchenko, A., Wilm, M., Vorm, O. & Mann, M. Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem. 68, 850–858 (1996).

    Article  CAS  PubMed  Google Scholar 

  116. Jungblut, P. R., Muller, E. C., Mattow, J. & Kaufmann, S. H. Proteomics reveals open reading frames in Mycobacterium tuberculosis H37Rv not predicted by genomics. Infect. Immun. 69, 5905–5907 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Jaffe, J. D. et al. The complete genome and proteome of Mycoplasma mobile. Genome Res. 14, 1447–1461 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Gupta, N. et al. Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation. Genome Res. 17, 1362–1377 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Fermin, D. et al. Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics. Genome Biol. 7, R35 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Merrihew, G. E. et al. Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations. Genome Res. 18, 1660–1669 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Castellana, N. E. et al. Discovery and revision of Arabidopsis genes by proteogenomics. Proc. Natl Acad. Sci. USA 105, 21034–21038 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Gupta, N. et al. Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes. Genome Res. 18, 1133–1142 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A. & White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl Acad. Sci. USA 104, 5860–5865 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Wepf, A., Glatter, T., Schmidt, A., Aebersold, R. & Gstaiger, M. Quantitative interaction proteomics using mass spectrometry. Nature Methods 6, 203–205 (2009).

    Article  CAS  PubMed  Google Scholar 

  125. Sadowski, P. G. et al. Quantitative proteomic approach to study subcellular localization of membrane proteins. Nature Protoc. 1, 1778–1789 (2006).

    Article  CAS  Google Scholar 

  126. Ahrens, C. H., Brunner, E., Hafen, E., Aebersold, R. & Basler, K. A proteome catalog of Drosophila melanogaster: an essential resource for targeted quantitative proteomics. Fly (Austin) 1, 182–186 (2007).

    Article  Google Scholar 

  127. Lange, V. et al. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol. Cell. Proteomics 7, 1489–1500 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Kuhner, S. et al. Proteome organization in a genome-reduced bacterium. Science 326, 1235–1240 (2009).

    Article  PubMed  CAS  Google Scholar 

  129. Skipp, P., Robinson, J., O'Connor, C. D. & Clarke, I. N. Shotgun proteomic analysis of Chlamydia trachomatis. Proteomics 5, 1558–1573 (2005).

    Article  CAS  PubMed  Google Scholar 

  130. Jungblut, P. R. et al. Helicobacter pylori proteomics by 2-DE/MS, 1-DE-LC/MS and functional data mining. Proteomics 10, 182–193 (2010).

    Article  CAS  PubMed  Google Scholar 

  131. Langen, H. et al. Two-dimensional map of the proteome of Haemophilus influenzae. Electrophoresis 21, 411–429 (2000).

    Article  CAS  PubMed  Google Scholar 

  132. Gan, R. R. et al. Proteome analysis of Halobacterium sp. NRC-1 facilitated by the biomodule analysis tool BMSorter. Mol. Cell. Proteomics 5, 987–997 (2006).

    Article  CAS  PubMed  Google Scholar 

  133. Van, P. T. et al. Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage. J. Proteome Res. 7, 3755–3764 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Becher, D. et al. A proteomic view of an important human pathogen — towards the quantification of the entire Staphylococcus aureus proteome. PLoS One 4, e8176 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Plikat, U. et al. From proteomics to systems biology of bacterial pathogens: approaches, tools, and applications. Proteomics 7, 992–1003 (2007).

    Article  CAS  PubMed  Google Scholar 

  136. Lipton, M. S. et al. Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags. Proc. Natl Acad. Sci. USA 99, 11049–11054 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Wolff, S. et al. Towards the entire proteome of the model bacterium Bacillus subtilis by gel-based and gel-free approaches. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 849, 129–140 (2007).

    Article  CAS  PubMed  Google Scholar 

  138. Iwasaki, M. et al. One-dimensional capillary liquid chromatographic separation coupled with tandem mass spectrometry unveils the Escherichia coli proteome on a microarray scale. Anal. Chem. 82, 2616–2620 (2010).

    Article  CAS  PubMed  Google Scholar 

  139. Henne, K. L. et al. Global proteomic analysis of the chromate response in Arthrobacter sp. strain FB24. J. Proteome Res. 8, 1704–1716 (2009).

    Article  CAS  PubMed  Google Scholar 

  140. Bosch, G. et al. Comprehensive proteomics of Methylobacterium extorquens AM1 metabolism under single carbon and nonmethylotrophic conditions. Proteomics 8, 3494–3505 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Hardman, M. & Makarov, A. A. Interfacing the orbitrap mass analyzer to an electrospray ion source. Anal. Chem. 75, 1699–1705 (2003).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

R.A. acknowledges support from the European Research Council (ERC-2008-AdG), the Swiss National Science Foundation (SNF) under Grant Number 31000-10767 and SystemsX.ch, the Swiss initiative for Systems Biology. C.H.A., E.B., E.Q., and K.B. are members of the Quantitative Model Organism Proteomics Initiative, which is funded in part by the University Research Priority Program Systems Biology/Functional Genomics of the University of Zurich. This work was also supported by SNF grant 31003A_130723 to C.H.A. and an UBS grant to E.B. and K.B.

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Correspondence to Christian H. Ahrens or Ruedi Aebersold.

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Supplementary information

Supplementary information S1 (figure) | Exponential increase of completed genome sequences over time. (PDF 222 kb)

41580_2010_BFnrm2973_MOESM2_ESM.pdf

Supplementary information S2 (Table) | Overview over selected publicly accessible proteomics database resources and tools to support targeted quantitative proteomics experiments. (PDF 419 kb)

Supplementary Information S3 (figure) | Protein identification error rate in large proteomics datasets. (PDF 248 kb)

41580_2010_BFnrm2973_MOESM4_ESM.pdf

Supplementary Information S4 (figure) | Over-representation of short proteins and proteins of predicted higher abundance among manually verified single hit protein identifications. (PDF 258 kb)

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FURTHER INFORMATION

Quantitative model organism proteomics homepage

Ruedi Aebersold's laboratory homepage

Nucleic Acids Research database issue

PeptideAtlas project

MRMAtlas

Glossary

Shotgun proteomics

A method for the identification of proteins in complex biological samples, also referred to as bottom-up proteomics or discovery proteomics. It combines the use of high-performance liquid chromatography to separate highly complex peptide mixtures and the subsequent identification of their sequence identity by MS/MS16 (see Box 1).

Information-rich peptide

The concept of using an in silico, precomputed classification of a peptide's ability to unambiguously imply one specific protein isoform, a subset of all isoforms, or all isoforms encoded by a gene model17,18. For this classification, the relationship between peptides, protein sequences and gene model information is assessed.

Precursor ion selection

The process of peptide ion selection in a mass spectrometer operated in shotgun mode. It follows a simple heuristic strategy, in which the precursor ion signals detected in a survey scan are selected on the basis of their signal intensity (typically in order of decreasing signal intensity) and charge state in high resolution mass spectrometers. Precursor ion selection is biased towards higher intensity precursor ion signals.

Collision-activated dissociation

(Also known as collision-induced dissociation (CID)). The mechanism by which molecular ions are fragmented in the collision cell of a mass spectrometer in the gas phase.

Protein inference

The process of assembling the experimentally observed but potentially ambiguous peptides into a list of proteins they best represent. Protein inference is usually carried out either by applying statistical models based on peptide scores for deriving a final protein score or by the deterministic and stringent filtering of non-ambiguously identified peptides (see Box 1).

Spectral counting

A method for the semi-quantitative estimation of protein abundance by summing up the respective total number of observed peptide spectra for a specific protein.

False discovery rate

The expected fraction of incorrect assignments among all accepted assignments for a selected threshold. It can be estimated at the PSM, peptide or protein level.

Target–decoy database search strategy

A method that extends the existing target search database with scrambled decoy sequences, usually by reversing the target sequences, in order to estimate and control the FDR for a specific search. Matches to the decoy database are considered false and the incidence of such false matches is used to compute the FDR of the matches to the relevant database.

SPOT synthesis technology

A cost efficient method for the rapid chemical synthesis of large numbers of peptides by a sequential stepwise addition of specific amino acids. Multiple such syntheses are carried out in parallel on a planar membrane support, increasing the throughput80.

Proteotypic peptide

A peptide that unambiguously identifies one protein; that is, a peptide that is unique within a protein database and is observable by MS.

Linear ion trap instrument

A device that allows ions to be stored in the mass spectrometer and mobilizes selected ion species based on their m/z ratio towards a detector or additional physical device. Linear ion traps are frequently used in conjunction with high-mass-accuracy mass spectrometers, such as orbitraps141 and fourier transform ion cyclotyron resonance (FT-ICR) mass spectrometers.

Triple quadrupole instrument

A type of mass spectrometer with three distinct mass analysers (quadrupoles). The quadrupoles are used to filter sample ions based on their m/z ratio. Typically, the second quadrupole acts as a collision cell.

Retention time

The time it takes for an analyte for a given chromatography and coupled mass spectrometer set-up to go from injection to being eluted (and subsequently identified).

Transition

A specific pair of m/z values associated with a precursor and a specific fragment ion of a proteotypic peptide that forms the basis for any quantitative measurement in the SRM mode.

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Ahrens, C., Brunner, E., Qeli, E. et al. Generating and navigating proteome maps using mass spectrometry. Nat Rev Mol Cell Biol 11, 789–801 (2010). https://doi.org/10.1038/nrm2973

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